Overview

Brought to you by YData

Dataset statistics

Number of variables25
Number of observations284
Missing cells74
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory55.6 KiB
Average record size in memory200.5 B

Variable types

Text18
Categorical7

Alerts

Butano is highly overall correlated with Diesel ultra bajo azufreHigh correlation
Diesel ultra bajo azufre is highly overall correlated with Butano and 2 other fieldsHigh correlation
MTBE is highly overall correlated with Petróleo reconstituidoHigh correlation
Naftas is highly overall correlated with Diesel ultra bajo azufre and 1 other fieldsHigh correlation
Petróleo crudo is highly overall correlated with Diesel ultra bajo azufre and 1 other fieldsHigh correlation
Petróleo reconstituido is highly overall correlated with MTBEHigh correlation
Diesel ultra bajo azufre is highly imbalanced (72.1%)Imbalance
Naftas is highly imbalanced (73.0%)Imbalance
Petróleo crudo is highly imbalanced (81.0%)Imbalance
Butano is highly imbalanced (76.0%)Imbalance
MTBE is highly imbalanced (89.6%)Imbalance
Orimulsión is highly imbalanced (88.4%)Imbalance
Petróleo reconstituido is highly imbalanced (84.6%)Imbalance
Aceites lubricantes has 3 (1.1%) missing valuesMissing
Asfalto has 3 (1.1%) missing valuesMissing
Bunker has 3 (1.1%) missing valuesMissing
Ceras has 4 (1.4%) missing valuesMissing
Combustible turbo jet has 3 (1.1%) missing valuesMissing
Diesel bajo azufre has 3 (1.1%) missing valuesMissing
Diesel ultra bajo azufre has 3 (1.1%) missing valuesMissing
Gas licuado de petróleo has 3 (1.1%) missing valuesMissing
Gasolina de aviación has 3 (1.1%) missing valuesMissing
Gasolina regular has 3 (1.1%) missing valuesMissing
Gasolina superior has 3 (1.1%) missing valuesMissing
Grasas lubricantes has 3 (1.1%) missing valuesMissing
Kerosina has 3 (1.1%) missing valuesMissing
Mezclas oleosas has 3 (1.1%) missing valuesMissing
Naftas has 3 (1.1%) missing valuesMissing
Petcoke has 3 (1.1%) missing valuesMissing
Petróleo crudo has 3 (1.1%) missing valuesMissing
Solventes has 3 (1.1%) missing valuesMissing
Butano has 3 (1.1%) missing valuesMissing
Diesel alto azufre has 3 (1.1%) missing valuesMissing
MTBE has 3 (1.1%) missing valuesMissing
Orimulsión has 3 (1.1%) missing valuesMissing
Petróleo reconstituido has 3 (1.1%) missing valuesMissing
Total importación has 3 (1.1%) missing valuesMissing

Reproduction

Analysis started2024-07-30 02:21:06.673461
Analysis finished2024-07-30 02:21:07.459409
Duration0.79 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Fecha
Text

Distinct283
Distinct (%)100.0%
Missing1
Missing (%)0.4%
Memory size2.3 KiB
2024-07-29T20:21:07.551935image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length145
Median length8
Mean length8.9010601
Min length8

Characters and Unicode

Total characters2519
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique283 ?
Unique (%)100.0%

Sample

1st rowene/2001
2nd rowfeb/2001
3rd rowmar/2001
4th rowabr/2001
5th rowmay/2001
ValueCountFrequency (%)
de 7
 
2.2%
mensuales 2
 
0.6%
informes 2
 
0.6%
por 2
 
0.6%
ene/2001 1
 
0.3%
mar/2001 1
 
0.3%
sept/2002 1
 
0.3%
ago/2002 1
 
0.3%
jul/2002 1
 
0.3%
jun/2002 1
 
0.3%
Other values (295) 295
93.9%
2024-07-29T20:21:07.715504image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 413
16.4%
2 370
14.7%
/ 281
 
11.2%
1 156
 
6.2%
e 123
 
4.9%
a 117
 
4.6%
n 85
 
3.4%
o 84
 
3.3%
r 62
 
2.5%
c 60
 
2.4%
Other values (33) 768
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2519
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 413
16.4%
2 370
14.7%
/ 281
 
11.2%
1 156
 
6.2%
e 123
 
4.9%
a 117
 
4.6%
n 85
 
3.4%
o 84
 
3.3%
r 62
 
2.5%
c 60
 
2.4%
Other values (33) 768
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2519
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 413
16.4%
2 370
14.7%
/ 281
 
11.2%
1 156
 
6.2%
e 123
 
4.9%
a 117
 
4.6%
n 85
 
3.4%
o 84
 
3.3%
r 62
 
2.5%
c 60
 
2.4%
Other values (33) 768
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2519
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 413
16.4%
2 370
14.7%
/ 281
 
11.2%
1 156
 
6.2%
e 123
 
4.9%
a 117
 
4.6%
n 85
 
3.4%
o 84
 
3.3%
r 62
 
2.5%
c 60
 
2.4%
Other values (33) 768
30.5%

Aceites lubricantes
Text

MISSING 

Distinct66
Distinct (%)23.5%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:07.817930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length4
Mean length5.1565836
Min length4

Characters and Unicode

Total characters1449
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)23.1%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 216
76.9%
16.064,15 1
 
0.4%
16.541,30 1
 
0.4%
17.450,33 1
 
0.4%
22.195,29 1
 
0.4%
22.041,50 1
 
0.4%
20.059,59 1
 
0.4%
19.268,71 1
 
0.4%
17.011,47 1
 
0.4%
19.109,12 1
 
0.4%
Other values (56) 56
 
19.9%
2024-07-29T20:21:07.955349image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 697
48.1%
, 281
19.4%
2 75
 
5.2%
. 65
 
4.5%
1 62
 
4.3%
9 44
 
3.0%
7 41
 
2.8%
3 41
 
2.8%
6 38
 
2.6%
5 38
 
2.6%
Other values (2) 67
 
4.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1449
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 697
48.1%
, 281
19.4%
2 75
 
5.2%
. 65
 
4.5%
1 62
 
4.3%
9 44
 
3.0%
7 41
 
2.8%
3 41
 
2.8%
6 38
 
2.6%
5 38
 
2.6%
Other values (2) 67
 
4.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1449
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 697
48.1%
, 281
19.4%
2 75
 
5.2%
. 65
 
4.5%
1 62
 
4.3%
9 44
 
3.0%
7 41
 
2.8%
3 41
 
2.8%
6 38
 
2.6%
5 38
 
2.6%
Other values (2) 67
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1449
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 697
48.1%
, 281
19.4%
2 75
 
5.2%
. 65
 
4.5%
1 62
 
4.3%
9 44
 
3.0%
7 41
 
2.8%
3 41
 
2.8%
6 38
 
2.6%
5 38
 
2.6%
Other values (2) 67
 
4.6%

Asfalto
Text

MISSING 

Distinct270
Distinct (%)96.1%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:08.081758image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.9964413
Min length4

Characters and Unicode

Total characters2247
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique267 ?
Unique (%)95.0%

Sample

1st row27.748,99
2nd row7.503,57
3rd row26.304,32
4th row7.885,89
5th row8.443,16
ValueCountFrequency (%)
0,00 10
 
3.6%
152,00 2
 
0.7%
1.285,71 2
 
0.7%
4.374,00 1
 
0.4%
25.060,00 1
 
0.4%
1.170,05 1
 
0.4%
26.304,32 1
 
0.4%
7.885,89 1
 
0.4%
8.443,16 1
 
0.4%
6.229,04 1
 
0.4%
Other values (260) 260
92.5%
2024-07-29T20:21:08.250331image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 324
14.4%
, 281
12.5%
. 255
11.3%
1 225
10.0%
4 174
7.7%
2 163
7.3%
3 156
6.9%
5 151
6.7%
8 145
6.5%
7 144
6.4%
Other values (2) 229
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2247
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 324
14.4%
, 281
12.5%
. 255
11.3%
1 225
10.0%
4 174
7.7%
2 163
7.3%
3 156
6.9%
5 151
6.7%
8 145
6.5%
7 144
6.4%
Other values (2) 229
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2247
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 324
14.4%
, 281
12.5%
. 255
11.3%
1 225
10.0%
4 174
7.7%
2 163
7.3%
3 156
6.9%
5 151
6.7%
8 145
6.5%
7 144
6.4%
Other values (2) 229
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2247
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 324
14.4%
, 281
12.5%
. 255
11.3%
1 225
10.0%
4 174
7.7%
2 163
7.3%
3 156
6.9%
5 151
6.7%
8 145
6.5%
7 144
6.4%
Other values (2) 229
10.2%

Bunker
Text

MISSING 

Distinct281
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:08.384621image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length9.8754448
Min length8

Characters and Unicode

Total characters2775
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)100.0%

Sample

1st row214.581,84
2nd row294.609,00
3rd row315.263,80
4th row205.653,00
5th row278.371,30
ValueCountFrequency (%)
400.963,62 1
 
0.4%
385.142,30 1
 
0.4%
205.653,00 1
 
0.4%
278.371,30 1
 
0.4%
218.765,60 1
 
0.4%
156.109,00 1
 
0.4%
189.692,60 1
 
0.4%
536.918,24 1
 
0.4%
142.282,26 1
 
0.4%
307.343,57 1
 
0.4%
Other values (271) 271
96.4%
2024-07-29T20:21:08.562004image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 282
10.2%
, 281
10.1%
2 267
9.6%
1 263
9.5%
3 260
9.4%
0 230
8.3%
4 228
8.2%
5 226
8.1%
6 203
7.3%
8 184
6.6%
Other values (2) 351
12.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2775
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 282
10.2%
, 281
10.1%
2 267
9.6%
1 263
9.5%
3 260
9.4%
0 230
8.3%
4 228
8.2%
5 226
8.1%
6 203
7.3%
8 184
6.6%
Other values (2) 351
12.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2775
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 282
10.2%
, 281
10.1%
2 267
9.6%
1 263
9.5%
3 260
9.4%
0 230
8.3%
4 228
8.2%
5 226
8.1%
6 203
7.3%
8 184
6.6%
Other values (2) 351
12.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2775
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 282
10.2%
, 281
10.1%
2 267
9.6%
1 263
9.5%
3 260
9.4%
0 230
8.3%
4 228
8.2%
5 226
8.1%
6 203
7.3%
8 184
6.6%
Other values (2) 351
12.6%

Ceras
Text

MISSING 

Distinct61
Distinct (%)21.8%
Missing4
Missing (%)1.4%
Memory size2.3 KiB
2024-07-29T20:21:08.656449image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length6
Median length4
Mean length4.3642857
Min length4

Characters and Unicode

Total characters1222
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)21.4%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 220
78.6%
59,00 1
 
0.4%
307,60 1
 
0.4%
426,69 1
 
0.4%
512,67 1
 
0.4%
171,98 1
 
0.4%
507,79 1
 
0.4%
202,29 1
 
0.4%
404,40 1
 
0.4%
557,90 1
 
0.4%
Other values (51) 51
 
18.2%
2024-07-29T20:21:08.792861image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 705
57.7%
, 280
 
22.9%
3 37
 
3.0%
1 31
 
2.5%
5 28
 
2.3%
9 26
 
2.1%
2 25
 
2.0%
4 25
 
2.0%
8 23
 
1.9%
7 22
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 705
57.7%
, 280
 
22.9%
3 37
 
3.0%
1 31
 
2.5%
5 28
 
2.3%
9 26
 
2.1%
2 25
 
2.0%
4 25
 
2.0%
8 23
 
1.9%
7 22
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 705
57.7%
, 280
 
22.9%
3 37
 
3.0%
1 31
 
2.5%
5 28
 
2.3%
9 26
 
2.1%
2 25
 
2.0%
4 25
 
2.0%
8 23
 
1.9%
7 22
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1222
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 705
57.7%
, 280
 
22.9%
3 37
 
3.0%
1 31
 
2.5%
5 28
 
2.3%
9 26
 
2.1%
2 25
 
2.0%
4 25
 
2.0%
8 23
 
1.9%
7 22
 
1.8%

Combustible turbo jet
Text

MISSING 

Distinct99
Distinct (%)35.2%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:08.907531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length10
Median length4
Mean length5.8149466
Min length4

Characters and Unicode

Total characters1634
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)34.9%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 183
65.1%
92.894,94 1
 
0.4%
97.343,58 1
 
0.4%
81.862,33 1
 
0.4%
108.804,20 1
 
0.4%
147.462,11 1
 
0.4%
94.858,75 1
 
0.4%
81.839,62 1
 
0.4%
95.032,90 1
 
0.4%
158.719,36 1
 
0.4%
Other values (89) 89
31.7%
2024-07-29T20:21:09.065668image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 615
37.6%
, 281
17.2%
. 98
 
6.0%
1 87
 
5.3%
4 79
 
4.8%
5 77
 
4.7%
7 72
 
4.4%
2 67
 
4.1%
9 66
 
4.0%
3 65
 
4.0%
Other values (2) 127
 
7.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 615
37.6%
, 281
17.2%
. 98
 
6.0%
1 87
 
5.3%
4 79
 
4.8%
5 77
 
4.7%
7 72
 
4.4%
2 67
 
4.1%
9 66
 
4.0%
3 65
 
4.0%
Other values (2) 127
 
7.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 615
37.6%
, 281
17.2%
. 98
 
6.0%
1 87
 
5.3%
4 79
 
4.8%
5 77
 
4.7%
7 72
 
4.4%
2 67
 
4.1%
9 66
 
4.0%
3 65
 
4.0%
Other values (2) 127
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 615
37.6%
, 281
17.2%
. 98
 
6.0%
1 87
 
5.3%
4 79
 
4.8%
5 77
 
4.7%
7 72
 
4.4%
2 67
 
4.1%
9 66
 
4.0%
3 65
 
4.0%
Other values (2) 127
 
7.8%

Diesel bajo azufre
Text

MISSING 

Distinct78
Distinct (%)27.8%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:09.177053image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length4
Mean length6.0569395
Min length4

Characters and Unicode

Total characters1702
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)27.4%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 204
72.6%
899.588,34 1
 
0.4%
1.592.580,34 1
 
0.4%
777.679,91 1
 
0.4%
793.683,41 1
 
0.4%
1.281.364,78 1
 
0.4%
896.266,43 1
 
0.4%
992.682,04 1
 
0.4%
1.153.831,89 1
 
0.4%
1.195.728,20 1
 
0.4%
Other values (68) 68
 
24.2%
2024-07-29T20:21:09.328560image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 687
40.4%
, 281
16.5%
. 135
 
7.9%
1 126
 
7.4%
9 74
 
4.3%
7 69
 
4.1%
2 62
 
3.6%
8 61
 
3.6%
6 60
 
3.5%
4 53
 
3.1%
Other values (2) 94
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1702
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 687
40.4%
, 281
16.5%
. 135
 
7.9%
1 126
 
7.4%
9 74
 
4.3%
7 69
 
4.1%
2 62
 
3.6%
8 61
 
3.6%
6 60
 
3.5%
4 53
 
3.1%
Other values (2) 94
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1702
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 687
40.4%
, 281
16.5%
. 135
 
7.9%
1 126
 
7.4%
9 74
 
4.3%
7 69
 
4.1%
2 62
 
3.6%
8 61
 
3.6%
6 60
 
3.5%
4 53
 
3.1%
Other values (2) 94
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1702
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 687
40.4%
, 281
16.5%
. 135
 
7.9%
1 126
 
7.4%
9 74
 
4.3%
7 69
 
4.1%
2 62
 
3.6%
8 61
 
3.6%
6 60
 
3.5%
4 53
 
3.1%
Other values (2) 94
 
5.5%

Diesel ultra bajo azufre
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct47
Distinct (%)16.7%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
235 
21.987,53
 
1
1.888,95
 
1
38.055,30
 
1
6.049,00
 
1
Other values (42)
42 

Length

Max length9
Median length4
Mean length4.7437722
Min length4

Characters and Unicode

Total characters1333
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)16.4%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00

Common Values

ValueCountFrequency (%)
0,00 235
82.7%
21.987,53 1
 
0.4%
1.888,95 1
 
0.4%
38.055,30 1
 
0.4%
6.049,00 1
 
0.4%
39.035,84 1
 
0.4%
39.866,57 1
 
0.4%
7.551,05 1
 
0.4%
35.005,92 1
 
0.4%
34.013,75 1
 
0.4%
Other values (37) 37
 
13.0%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:09.388856image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 235
83.6%
45.074,97 1
 
0.4%
29.946,48 1
 
0.4%
4.985,21 1
 
0.4%
4.034,55 1
 
0.4%
9.040,11 1
 
0.4%
24.651,72 1
 
0.4%
25.727,21 1
 
0.4%
4.983,86 1
 
0.4%
11.260,59 1
 
0.4%
Other values (37) 37
 
13.2%

Most occurring characters

ValueCountFrequency (%)
0 744
55.8%
, 281
 
21.1%
9 48
 
3.6%
. 46
 
3.5%
5 37
 
2.8%
1 32
 
2.4%
4 30
 
2.3%
8 28
 
2.1%
2 25
 
1.9%
3 25
 
1.9%
Other values (2) 37
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 744
55.8%
, 281
 
21.1%
9 48
 
3.6%
. 46
 
3.5%
5 37
 
2.8%
1 32
 
2.4%
4 30
 
2.3%
8 28
 
2.1%
2 25
 
1.9%
3 25
 
1.9%
Other values (2) 37
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 744
55.8%
, 281
 
21.1%
9 48
 
3.6%
. 46
 
3.5%
5 37
 
2.8%
1 32
 
2.4%
4 30
 
2.3%
8 28
 
2.1%
2 25
 
1.9%
3 25
 
1.9%
Other values (2) 37
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 744
55.8%
, 281
 
21.1%
9 48
 
3.6%
. 46
 
3.5%
5 37
 
2.8%
1 32
 
2.4%
4 30
 
2.3%
8 28
 
2.1%
2 25
 
1.9%
3 25
 
1.9%
Other values (2) 37
 
2.8%
Distinct281
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:09.502169image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.007117
Min length10

Characters and Unicode

Total characters2812
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)100.0%

Sample

1st row194.065,74
2nd row170.703,38
3rd row161.837,37
4th row163.048,64
5th row171.518,86
ValueCountFrequency (%)
703.319,23 1
 
0.4%
186.839,02 1
 
0.4%
163.048,64 1
 
0.4%
171.518,86 1
 
0.4%
190.004,42 1
 
0.4%
206.022,83 1
 
0.4%
100.561,47 1
 
0.4%
216.302,81 1
 
0.4%
194.722,50 1
 
0.4%
159.299,42 1
 
0.4%
Other values (271) 271
96.4%
2024-07-29T20:21:09.683560image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
1 277
9.9%
4 248
8.8%
2 236
8.4%
6 234
8.3%
5 230
8.2%
8 227
8.1%
3 225
8.0%
0 199
7.1%
Other values (2) 373
13.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
1 277
9.9%
4 248
8.8%
2 236
8.4%
6 234
8.3%
5 230
8.2%
8 227
8.1%
3 225
8.0%
0 199
7.1%
Other values (2) 373
13.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
1 277
9.9%
4 248
8.8%
2 236
8.4%
6 234
8.3%
5 230
8.2%
8 227
8.1%
3 225
8.0%
0 199
7.1%
Other values (2) 373
13.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
1 277
9.9%
4 248
8.8%
2 236
8.4%
6 234
8.3%
5 230
8.2%
8 227
8.1%
3 225
8.0%
0 199
7.1%
Other values (2) 373
13.3%
Distinct165
Distinct (%)58.7%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:09.813312image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.024911
Min length4

Characters and Unicode

Total characters1693
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique158 ?
Unique (%)56.2%

Sample

1st row820,00
2nd row3.054,00
3rd row677,00
4th row3.399,00
5th row585,00
ValueCountFrequency (%)
0,00 109
38.8%
190,50 3
 
1.1%
381,00 3
 
1.1%
95,20 2
 
0.7%
285,70 2
 
0.7%
190,00 2
 
0.7%
452,00 2
 
0.7%
2.988,00 1
 
0.4%
677,00 1
 
0.4%
3.399,00 1
 
0.4%
Other values (155) 155
55.2%
2024-07-29T20:21:09.984212image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 599
35.4%
, 281
16.6%
. 107
 
6.3%
3 99
 
5.8%
2 96
 
5.7%
1 86
 
5.1%
7 83
 
4.9%
5 78
 
4.6%
6 69
 
4.1%
4 68
 
4.0%
Other values (2) 127
 
7.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 599
35.4%
, 281
16.6%
. 107
 
6.3%
3 99
 
5.8%
2 96
 
5.7%
1 86
 
5.1%
7 83
 
4.9%
5 78
 
4.6%
6 69
 
4.1%
4 68
 
4.0%
Other values (2) 127
 
7.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 599
35.4%
, 281
16.6%
. 107
 
6.3%
3 99
 
5.8%
2 96
 
5.7%
1 86
 
5.1%
7 83
 
4.9%
5 78
 
4.6%
6 69
 
4.1%
4 68
 
4.0%
Other values (2) 127
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 599
35.4%
, 281
16.6%
. 107
 
6.3%
3 99
 
5.8%
2 96
 
5.7%
1 86
 
5.1%
7 83
 
4.9%
5 78
 
4.6%
6 69
 
4.1%
4 68
 
4.0%
Other values (2) 127
 
7.5%

Gasolina regular
Text

MISSING 

Distinct281
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:10.125082image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9928826
Min length9

Characters and Unicode

Total characters2808
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)100.0%

Sample

1st row177.776,50
2nd row123.115,99
3rd row161.726,42
4th row127.338,74
5th row168.730,19
ValueCountFrequency (%)
397.477,40 1
 
0.4%
233.643,75 1
 
0.4%
127.338,74 1
 
0.4%
168.730,19 1
 
0.4%
152.899,09 1
 
0.4%
136.299,13 1
 
0.4%
139.365,07 1
 
0.4%
181.668,03 1
 
0.4%
165.841,46 1
 
0.4%
194.830,10 1
 
0.4%
Other values (271) 271
96.4%
2024-07-29T20:21:10.300747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 281
10.0%
, 281
10.0%
3 269
9.6%
2 266
9.5%
1 244
8.7%
4 228
8.1%
7 226
8.0%
8 218
7.8%
0 208
7.4%
6 201
7.2%
Other values (2) 386
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2808
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 281
10.0%
, 281
10.0%
3 269
9.6%
2 266
9.5%
1 244
8.7%
4 228
8.1%
7 226
8.0%
8 218
7.8%
0 208
7.4%
6 201
7.2%
Other values (2) 386
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2808
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 281
10.0%
, 281
10.0%
3 269
9.6%
2 266
9.5%
1 244
8.7%
4 228
8.1%
7 226
8.0%
8 218
7.8%
0 208
7.4%
6 201
7.2%
Other values (2) 386
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2808
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 281
10.0%
, 281
10.0%
3 269
9.6%
2 266
9.5%
1 244
8.7%
4 228
8.1%
7 226
8.0%
8 218
7.8%
0 208
7.4%
6 201
7.2%
Other values (2) 386
13.7%

Gasolina superior
Text

MISSING 

Distinct281
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:10.441007image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length10.007117
Min length10

Characters and Unicode

Total characters2812
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)100.0%

Sample

1st row373.963,96
2nd row243.091,07
3rd row312.084,38
4th row285.054,89
5th row300.913,67
ValueCountFrequency (%)
502.082,45 1
 
0.4%
308.439,07 1
 
0.4%
285.054,89 1
 
0.4%
300.913,67 1
 
0.4%
333.217,19 1
 
0.4%
195.071,86 1
 
0.4%
268.153,26 1
 
0.4%
260.219,92 1
 
0.4%
256.638,19 1
 
0.4%
363.311,76 1
 
0.4%
Other values (271) 271
96.4%
2024-07-29T20:21:10.624424image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
3 279
9.9%
5 267
9.5%
4 235
8.4%
2 228
8.1%
6 226
8.0%
7 209
7.4%
0 206
7.3%
1 205
7.3%
Other values (2) 394
14.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2812
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
3 279
9.9%
5 267
9.5%
4 235
8.4%
2 228
8.1%
6 226
8.0%
7 209
7.4%
0 206
7.3%
1 205
7.3%
Other values (2) 394
14.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2812
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
3 279
9.9%
5 267
9.5%
4 235
8.4%
2 228
8.1%
6 226
8.0%
7 209
7.4%
0 206
7.3%
1 205
7.3%
Other values (2) 394
14.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2812
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 282
10.0%
, 281
10.0%
3 279
9.9%
5 267
9.5%
4 235
8.4%
2 228
8.1%
6 226
8.0%
7 209
7.4%
0 206
7.3%
1 205
7.3%
Other values (2) 394
14.0%

Grasas lubricantes
Text

MISSING 

Distinct66
Distinct (%)23.5%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:10.724033image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.5551601
Min length4

Characters and Unicode

Total characters1280
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)23.1%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 216
76.9%
507,39 1
 
0.4%
585,91 1
 
0.4%
477,01 1
 
0.4%
489,43 1
 
0.4%
225,87 1
 
0.4%
117,68 1
 
0.4%
176,82 1
 
0.4%
348,21 1
 
0.4%
115,83 1
 
0.4%
Other values (56) 56
 
19.9%
2024-07-29T20:21:10.863266image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 675
52.7%
, 281
22.0%
3 42
 
3.3%
2 40
 
3.1%
4 39
 
3.0%
6 37
 
2.9%
9 36
 
2.8%
1 33
 
2.6%
7 29
 
2.3%
5 28
 
2.2%
Other values (2) 40
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 675
52.7%
, 281
22.0%
3 42
 
3.3%
2 40
 
3.1%
4 39
 
3.0%
6 37
 
2.9%
9 36
 
2.8%
1 33
 
2.6%
7 29
 
2.3%
5 28
 
2.2%
Other values (2) 40
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 675
52.7%
, 281
22.0%
3 42
 
3.3%
2 40
 
3.1%
4 39
 
3.0%
6 37
 
2.9%
9 36
 
2.8%
1 33
 
2.6%
7 29
 
2.3%
5 28
 
2.2%
Other values (2) 40
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 675
52.7%
, 281
22.0%
3 42
 
3.3%
2 40
 
3.1%
4 39
 
3.0%
6 37
 
2.9%
9 36
 
2.8%
1 33
 
2.6%
7 29
 
2.3%
5 28
 
2.2%
Other values (2) 40
 
3.1%

Kerosina
Text

MISSING 

Distinct194
Distinct (%)69.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:10.973524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.5053381
Min length4

Characters and Unicode

Total characters2109
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique192 ?
Unique (%)68.3%

Sample

1st row33.834,03
2nd row67.439,95
3rd row31.787,29
4th row25.801,18
5th row45.529,33
ValueCountFrequency (%)
0,00 86
30.6%
16.000,00 3
 
1.1%
20.757,68 1
 
0.4%
33.324,44 1
 
0.4%
25.801,18 1
 
0.4%
45.529,33 1
 
0.4%
64.444,95 1
 
0.4%
23.654,10 1
 
0.4%
49.890,61 1
 
0.4%
41.519,91 1
 
0.4%
Other values (184) 184
65.5%
2024-07-29T20:21:11.126077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 531
25.2%
, 281
13.3%
. 195
 
9.2%
1 133
 
6.3%
6 133
 
6.3%
3 129
 
6.1%
5 128
 
6.1%
4 123
 
5.8%
9 121
 
5.7%
7 117
 
5.5%
Other values (2) 218
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2109
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 531
25.2%
, 281
13.3%
. 195
 
9.2%
1 133
 
6.3%
6 133
 
6.3%
3 129
 
6.1%
5 128
 
6.1%
4 123
 
5.8%
9 121
 
5.7%
7 117
 
5.5%
Other values (2) 218
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2109
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 531
25.2%
, 281
13.3%
. 195
 
9.2%
1 133
 
6.3%
6 133
 
6.3%
3 129
 
6.1%
5 128
 
6.1%
4 123
 
5.8%
9 121
 
5.7%
7 117
 
5.5%
Other values (2) 218
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2109
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 531
25.2%
, 281
13.3%
. 195
 
9.2%
1 133
 
6.3%
6 133
 
6.3%
3 129
 
6.1%
5 128
 
6.1%
4 123
 
5.8%
9 121
 
5.7%
7 117
 
5.5%
Other values (2) 218
10.3%

Mezclas oleosas
Text

MISSING 

Distinct59
Distinct (%)21.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:11.221712image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length8
Median length4
Mean length4.7758007
Min length4

Characters and Unicode

Total characters1342
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)20.6%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 223
79.4%
356,40 1
 
0.4%
2.212,32 1
 
0.4%
1.944,32 1
 
0.4%
2.658,48 1
 
0.4%
3.134,83 1
 
0.4%
952,38 1
 
0.4%
166,67 1
 
0.4%
2.499,43 1
 
0.4%
2.579,15 1
 
0.4%
Other values (49) 49
 
17.4%
2024-07-29T20:21:11.415272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 685
51.0%
, 281
20.9%
2 57
 
4.2%
. 51
 
3.8%
1 43
 
3.2%
3 40
 
3.0%
9 36
 
2.7%
7 33
 
2.5%
6 33
 
2.5%
5 30
 
2.2%
Other values (2) 53
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1342
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 685
51.0%
, 281
20.9%
2 57
 
4.2%
. 51
 
3.8%
1 43
 
3.2%
3 40
 
3.0%
9 36
 
2.7%
7 33
 
2.5%
6 33
 
2.5%
5 30
 
2.2%
Other values (2) 53
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1342
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 685
51.0%
, 281
20.9%
2 57
 
4.2%
. 51
 
3.8%
1 43
 
3.2%
3 40
 
3.0%
9 36
 
2.7%
7 33
 
2.5%
6 33
 
2.5%
5 30
 
2.2%
Other values (2) 53
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1342
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 685
51.0%
, 281
20.9%
2 57
 
4.2%
. 51
 
3.8%
1 43
 
3.2%
3 40
 
3.0%
9 36
 
2.7%
7 33
 
2.5%
6 33
 
2.5%
5 30
 
2.2%
Other values (2) 53
 
3.9%

Naftas
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct43
Distinct (%)15.3%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
237 
40,00
 
3
414,41
 
1
360,06
 
1
259,99
 
1
Other values (38)
38 

Length

Max length6
Median length4
Mean length4.1779359
Min length4

Characters and Unicode

Total characters1174
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)14.6%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00

Common Values

ValueCountFrequency (%)
0,00 237
83.5%
40,00 3
 
1.1%
414,41 1
 
0.4%
360,06 1
 
0.4%
259,99 1
 
0.4%
350,92 1
 
0.4%
247,67 1
 
0.4%
1,14 1
 
0.4%
0,38 1
 
0.4%
60,00 1
 
0.4%
Other values (33) 33
 
11.6%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:11.474991image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 237
84.3%
40,00 3
 
1.1%
80,00 1
 
0.4%
123,60 1
 
0.4%
242,22 1
 
0.4%
263,28 1
 
0.4%
340,10 1
 
0.4%
256,92 1
 
0.4%
43,09 1
 
0.4%
380,64 1
 
0.4%
Other values (33) 33
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 752
64.1%
, 281
 
23.9%
2 25
 
2.1%
4 22
 
1.9%
3 19
 
1.6%
1 16
 
1.4%
6 15
 
1.3%
5 14
 
1.2%
9 12
 
1.0%
8 12
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 752
64.1%
, 281
 
23.9%
2 25
 
2.1%
4 22
 
1.9%
3 19
 
1.6%
1 16
 
1.4%
6 15
 
1.3%
5 14
 
1.2%
9 12
 
1.0%
8 12
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 752
64.1%
, 281
 
23.9%
2 25
 
2.1%
4 22
 
1.9%
3 19
 
1.6%
1 16
 
1.4%
6 15
 
1.3%
5 14
 
1.2%
9 12
 
1.0%
8 12
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 752
64.1%
, 281
 
23.9%
2 25
 
2.1%
4 22
 
1.9%
3 19
 
1.6%
1 16
 
1.4%
6 15
 
1.3%
5 14
 
1.2%
9 12
 
1.0%
8 12
 
1.0%

Petcoke
Text

MISSING 

Distinct149
Distinct (%)53.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:11.580217image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length10
Median length10
Mean length7.1530249
Min length4

Characters and Unicode

Total characters2010
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)52.7%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 133
47.3%
145.937,83 1
 
0.4%
134.700,50 1
 
0.4%
155.914,00 1
 
0.4%
146.223,00 1
 
0.4%
152.894,50 1
 
0.4%
141.267,50 1
 
0.4%
147.361,50 1
 
0.4%
137.890,50 1
 
0.4%
147.994,00 1
 
0.4%
Other values (139) 139
49.5%
2024-07-29T20:21:11.745332image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 575
28.6%
, 281
14.0%
1 165
 
8.2%
5 150
 
7.5%
. 148
 
7.4%
4 127
 
6.3%
2 109
 
5.4%
6 104
 
5.2%
7 98
 
4.9%
8 95
 
4.7%
Other values (2) 158
 
7.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2010
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 575
28.6%
, 281
14.0%
1 165
 
8.2%
5 150
 
7.5%
. 148
 
7.4%
4 127
 
6.3%
2 109
 
5.4%
6 104
 
5.2%
7 98
 
4.9%
8 95
 
4.7%
Other values (2) 158
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2010
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 575
28.6%
, 281
14.0%
1 165
 
8.2%
5 150
 
7.5%
. 148
 
7.4%
4 127
 
6.3%
2 109
 
5.4%
6 104
 
5.2%
7 98
 
4.9%
8 95
 
4.7%
Other values (2) 158
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2010
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 575
28.6%
, 281
14.0%
1 165
 
8.2%
5 150
 
7.5%
. 148
 
7.4%
4 127
 
6.3%
2 109
 
5.4%
6 104
 
5.2%
7 98
 
4.9%
8 95
 
4.7%
Other values (2) 158
 
7.9%

Petróleo crudo
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct28
Distinct (%)10.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
254 
1.474,00
 
1
1.014,29
 
1
1.005,96
 
1
4.115,91
 
1
Other values (23)
 
23

Length

Max length8
Median length4
Mean length4.3274021
Min length4

Characters and Unicode

Total characters1216
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)9.6%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00

Common Values

ValueCountFrequency (%)
0,00 254
89.4%
1.474,00 1
 
0.4%
1.014,29 1
 
0.4%
1.005,96 1
 
0.4%
4.115,91 1
 
0.4%
828,70 1
 
0.4%
1.604,92 1
 
0.4%
2.090,11 1
 
0.4%
2.264,37 1
 
0.4%
1.255,96 1
 
0.4%
Other values (18) 18
 
6.3%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:11.809933image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 254
90.4%
1.474,00 1
 
0.4%
937,08 1
 
0.4%
1.811,06 1
 
0.4%
3.845,00 1
 
0.4%
7.802,00 1
 
0.4%
2.869,00 1
 
0.4%
2.057,00 1
 
0.4%
3.190,00 1
 
0.4%
3.715,00 1
 
0.4%
Other values (18) 18
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0 808
66.4%
, 281
 
23.1%
1 27
 
2.2%
. 19
 
1.6%
2 18
 
1.5%
9 12
 
1.0%
4 9
 
0.7%
5 9
 
0.7%
6 9
 
0.7%
3 9
 
0.7%
Other values (2) 15
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1216
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 808
66.4%
, 281
 
23.1%
1 27
 
2.2%
. 19
 
1.6%
2 18
 
1.5%
9 12
 
1.0%
4 9
 
0.7%
5 9
 
0.7%
6 9
 
0.7%
3 9
 
0.7%
Other values (2) 15
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1216
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 808
66.4%
, 281
 
23.1%
1 27
 
2.2%
. 19
 
1.6%
2 18
 
1.5%
9 12
 
1.0%
4 9
 
0.7%
5 9
 
0.7%
6 9
 
0.7%
3 9
 
0.7%
Other values (2) 15
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1216
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 808
66.4%
, 281
 
23.1%
1 27
 
2.2%
. 19
 
1.6%
2 18
 
1.5%
9 12
 
1.0%
4 9
 
0.7%
5 9
 
0.7%
6 9
 
0.7%
3 9
 
0.7%
Other values (2) 15
 
1.2%

Solventes
Text

MISSING 

Distinct66
Distinct (%)23.5%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:11.887917image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length9
Median length4
Mean length5.024911
Min length4

Characters and Unicode

Total characters1412
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)23.1%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00
ValueCountFrequency (%)
0,00 216
76.9%
6.147,08 1
 
0.4%
7.282,59 1
 
0.4%
5.112,74 1
 
0.4%
6.263,91 1
 
0.4%
9.967,36 1
 
0.4%
9.079,17 1
 
0.4%
3.968,23 1
 
0.4%
8.867,90 1
 
0.4%
7.671,02 1
 
0.4%
Other values (56) 56
 
19.9%
2024-07-29T20:21:12.021197image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 686
48.6%
, 281
19.9%
1 65
 
4.6%
. 63
 
4.5%
7 47
 
3.3%
3 47
 
3.3%
4 43
 
3.0%
9 39
 
2.8%
8 38
 
2.7%
2 37
 
2.6%
Other values (2) 66
 
4.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1412
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 686
48.6%
, 281
19.9%
1 65
 
4.6%
. 63
 
4.5%
7 47
 
3.3%
3 47
 
3.3%
4 43
 
3.0%
9 39
 
2.8%
8 38
 
2.7%
2 37
 
2.6%
Other values (2) 66
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1412
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 686
48.6%
, 281
19.9%
1 65
 
4.6%
. 63
 
4.5%
7 47
 
3.3%
3 47
 
3.3%
4 43
 
3.0%
9 39
 
2.8%
8 38
 
2.7%
2 37
 
2.6%
Other values (2) 66
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1412
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 686
48.6%
, 281
19.9%
1 65
 
4.6%
. 63
 
4.5%
7 47
 
3.3%
3 47
 
3.3%
4 43
 
3.0%
9 39
 
2.8%
8 38
 
2.7%
2 37
 
2.6%
Other values (2) 66
 
4.7%

Butano
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct24
Distinct (%)8.5%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
242 
72,29
 
13
144,57
 
5
147,59
 
1
6,96
 
1
Other values (19)
 
19

Length

Max length6
Median length4
Mean length4.1459075
Min length4

Characters and Unicode

Total characters1165
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)7.5%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00

Common Values

ValueCountFrequency (%)
0,00 242
85.2%
72,29 13
 
4.6%
144,57 5
 
1.8%
147,59 1
 
0.4%
6,96 1
 
0.4%
5,84 1
 
0.4%
74,63 1
 
0.4%
74,94 1
 
0.4%
73,85 1
 
0.4%
83,27 1
 
0.4%
Other values (14) 14
 
4.9%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:12.082564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 242
86.1%
72,29 13
 
4.6%
144,57 5
 
1.8%
7,34 1
 
0.4%
0,37 1
 
0.4%
82,01 1
 
0.4%
72,86 1
 
0.4%
148,95 1
 
0.4%
13,01 1
 
0.4%
149,09 1
 
0.4%
Other values (14) 14
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 734
63.0%
, 281
 
24.1%
7 32
 
2.7%
2 29
 
2.5%
9 22
 
1.9%
4 20
 
1.7%
1 13
 
1.1%
5 11
 
0.9%
8 10
 
0.9%
3 9
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1165
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 734
63.0%
, 281
 
24.1%
7 32
 
2.7%
2 29
 
2.5%
9 22
 
1.9%
4 20
 
1.7%
1 13
 
1.1%
5 11
 
0.9%
8 10
 
0.9%
3 9
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1165
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 734
63.0%
, 281
 
24.1%
7 32
 
2.7%
2 29
 
2.5%
9 22
 
1.9%
4 20
 
1.7%
1 13
 
1.1%
5 11
 
0.9%
8 10
 
0.9%
3 9
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1165
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 734
63.0%
, 281
 
24.1%
7 32
 
2.7%
2 29
 
2.5%
9 22
 
1.9%
4 20
 
1.7%
1 13
 
1.1%
5 11
 
0.9%
8 10
 
0.9%
3 9
 
0.8%

Diesel alto azufre
Text

MISSING 

Distinct205
Distinct (%)73.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:12.179839image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length8.5978648
Min length4

Characters and Unicode

Total characters2416
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique204 ?
Unique (%)72.6%

Sample

1st row566.101,99
2nd row489.525,80
3rd row575.559,68
4th row437.745,42
5th row552.609,13
ValueCountFrequency (%)
0,00 77
 
27.4%
686.923,83 1
 
0.4%
575.559,68 1
 
0.4%
437.745,42 1
 
0.4%
552.609,13 1
 
0.4%
497.855,26 1
 
0.4%
302.350,02 1
 
0.4%
464.159,13 1
 
0.4%
321.952,94 1
 
0.4%
438.989,55 1
 
0.4%
Other values (195) 195
69.4%
2024-07-29T20:21:12.339679image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 378
15.6%
, 281
11.6%
. 238
9.9%
8 183
7.6%
7 180
7.5%
6 173
7.2%
4 172
7.1%
1 169
7.0%
5 165
6.8%
3 164
6.8%
Other values (2) 313
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 378
15.6%
, 281
11.6%
. 238
9.9%
8 183
7.6%
7 180
7.5%
6 173
7.2%
4 172
7.1%
1 169
7.0%
5 165
6.8%
3 164
6.8%
Other values (2) 313
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 378
15.6%
, 281
11.6%
. 238
9.9%
8 183
7.6%
7 180
7.5%
6 173
7.2%
4 172
7.1%
1 169
7.0%
5 165
6.8%
3 164
6.8%
Other values (2) 313
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 378
15.6%
, 281
11.6%
. 238
9.9%
8 183
7.6%
7 180
7.5%
6 173
7.2%
4 172
7.1%
1 169
7.0%
5 165
6.8%
3 164
6.8%
Other values (2) 313
13.0%

MTBE
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct12
Distinct (%)4.3%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
270 
8.402,00
 
1
8.184,00
 
1
12.680,00
 
1
10.642,00
 
1
Other values (7)
 
7

Length

Max length9
Median length4
Mean length4.1886121
Min length4

Characters and Unicode

Total characters1177
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)3.9%

Sample

1st row8.402,00
2nd row0,00
3rd row0,00
4th row8.184,00
5th row12.680,00

Common Values

ValueCountFrequency (%)
0,00 270
95.1%
8.402,00 1
 
0.4%
8.184,00 1
 
0.4%
12.680,00 1
 
0.4%
10.642,00 1
 
0.4%
13.357,00 1
 
0.4%
19.431,00 1
 
0.4%
14.822,00 1
 
0.4%
11.481,00 1
 
0.4%
12.350,00 1
 
0.4%
Other values (2) 2
 
0.7%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:12.401374image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 270
96.1%
8.402,00 1
 
0.4%
8.184,00 1
 
0.4%
12.680,00 1
 
0.4%
10.642,00 1
 
0.4%
13.357,00 1
 
0.4%
19.431,00 1
 
0.4%
14.822,00 1
 
0.4%
11.481,00 1
 
0.4%
12.350,00 1
 
0.4%
Other values (2) 2
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 837
71.1%
, 281
 
23.9%
1 13
 
1.1%
. 11
 
0.9%
8 8
 
0.7%
2 7
 
0.6%
4 6
 
0.5%
3 6
 
0.5%
6 2
 
0.2%
5 2
 
0.2%
Other values (2) 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1177
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 837
71.1%
, 281
 
23.9%
1 13
 
1.1%
. 11
 
0.9%
8 8
 
0.7%
2 7
 
0.6%
4 6
 
0.5%
3 6
 
0.5%
6 2
 
0.2%
5 2
 
0.2%
Other values (2) 4
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1177
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 837
71.1%
, 281
 
23.9%
1 13
 
1.1%
. 11
 
0.9%
8 8
 
0.7%
2 7
 
0.6%
4 6
 
0.5%
3 6
 
0.5%
6 2
 
0.2%
5 2
 
0.2%
Other values (2) 4
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1177
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 837
71.1%
, 281
 
23.9%
1 13
 
1.1%
. 11
 
0.9%
8 8
 
0.7%
2 7
 
0.6%
4 6
 
0.5%
3 6
 
0.5%
6 2
 
0.2%
5 2
 
0.2%
Other values (2) 4
 
0.3%

Orimulsión
Categorical

IMBALANCE  MISSING 

Distinct14
Distinct (%)5.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
268 
249.982,70
 
1
311.711,21
 
1
311.270,32
 
1
315.915,92
 
1
Other values (9)
 
9

Length

Max length10
Median length4
Mean length4.2775801
Min length4

Characters and Unicode

Total characters1202
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)4.6%

Sample

1st row0,00
2nd row0,00
3rd row0,00
4th row0,00
5th row0,00

Common Values

ValueCountFrequency (%)
0,00 268
94.4%
249.982,70 1
 
0.4%
311.711,21 1
 
0.4%
311.270,32 1
 
0.4%
315.915,92 1
 
0.4%
313.440,96 1
 
0.4%
318.754,00 1
 
0.4%
338.810,04 1
 
0.4%
316.798,00 1
 
0.4%
344.685,00 1
 
0.4%
Other values (4) 4
 
1.4%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:12.439754image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 268
95.4%
249.982,70 1
 
0.4%
311.711,21 1
 
0.4%
311.270,32 1
 
0.4%
315.915,92 1
 
0.4%
313.440,96 1
 
0.4%
318.754,00 1
 
0.4%
338.810,04 1
 
0.4%
316.798,00 1
 
0.4%
344.685,00 1
 
0.4%
Other values (4) 4
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0 826
68.7%
, 281
 
23.4%
3 18
 
1.5%
1 15
 
1.2%
. 13
 
1.1%
2 10
 
0.8%
4 8
 
0.7%
9 8
 
0.7%
8 7
 
0.6%
7 6
 
0.5%
Other values (2) 10
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 826
68.7%
, 281
 
23.4%
3 18
 
1.5%
1 15
 
1.2%
. 13
 
1.1%
2 10
 
0.8%
4 8
 
0.7%
9 8
 
0.7%
8 7
 
0.6%
7 6
 
0.5%
Other values (2) 10
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 826
68.7%
, 281
 
23.4%
3 18
 
1.5%
1 15
 
1.2%
. 13
 
1.1%
2 10
 
0.8%
4 8
 
0.7%
9 8
 
0.7%
8 7
 
0.6%
7 6
 
0.5%
Other values (2) 10
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 826
68.7%
, 281
 
23.4%
3 18
 
1.5%
1 15
 
1.2%
. 13
 
1.1%
2 10
 
0.8%
4 8
 
0.7%
9 8
 
0.7%
8 7
 
0.6%
7 6
 
0.5%
Other values (2) 10
 
0.8%

Petróleo reconstituido
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct21
Distinct (%)7.5%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
0,00
261 
360.530,00
 
1
359.527,00
 
1
723.346,00
 
1
360.369,00
 
1
Other values (16)
 
16

Length

Max length10
Median length4
Mean length4.4270463
Min length4

Characters and Unicode

Total characters1244
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)7.1%

Sample

1st row715.344,00
2nd row370.166,00
3rd row360.530,00
4th row359.527,00
5th row723.346,00

Common Values

ValueCountFrequency (%)
0,00 261
91.9%
360.530,00 1
 
0.4%
359.527,00 1
 
0.4%
723.346,00 1
 
0.4%
360.369,00 1
 
0.4%
360.570,00 1
 
0.4%
719.303,00 1
 
0.4%
360.635,00 1
 
0.4%
717.717,00 1
 
0.4%
356.364,00 1
 
0.4%
Other values (11) 11
 
3.9%
(Missing) 3
 
1.1%

Length

2024-07-29T20:21:12.479531image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0,00 261
92.9%
724.812,00 1
 
0.4%
370.166,00 1
 
0.4%
364.878,00 1
 
0.4%
365.679,00 1
 
0.4%
726.775,00 1
 
0.4%
369.420,00 1
 
0.4%
365.663,00 1
 
0.4%
730.957,00 1
 
0.4%
368.318,00 1
 
0.4%
Other values (11) 11
 
3.9%

Most occurring characters

ValueCountFrequency (%)
0 833
67.0%
, 281
 
22.6%
3 25
 
2.0%
6 22
 
1.8%
. 20
 
1.6%
7 18
 
1.4%
5 11
 
0.9%
2 7
 
0.6%
4 7
 
0.6%
1 7
 
0.6%
Other values (2) 13
 
1.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1244
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 833
67.0%
, 281
 
22.6%
3 25
 
2.0%
6 22
 
1.8%
. 20
 
1.6%
7 18
 
1.4%
5 11
 
0.9%
2 7
 
0.6%
4 7
 
0.6%
1 7
 
0.6%
Other values (2) 13
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1244
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 833
67.0%
, 281
 
22.6%
3 25
 
2.0%
6 22
 
1.8%
. 20
 
1.6%
7 18
 
1.4%
5 11
 
0.9%
2 7
 
0.6%
4 7
 
0.6%
1 7
 
0.6%
Other values (2) 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1244
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 833
67.0%
, 281
 
22.6%
3 25
 
2.0%
6 22
 
1.8%
. 20
 
1.6%
7 18
 
1.4%
5 11
 
0.9%
2 7
 
0.6%
4 7
 
0.6%
1 7
 
0.6%
Other values (2) 13
 
1.0%

Total importación
Text

MISSING 

Distinct281
Distinct (%)100.0%
Missing3
Missing (%)1.1%
Memory size2.3 KiB
2024-07-29T20:21:12.584641image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3372
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique281 ?
Unique (%)100.0%

Sample

1st row2.312.639,05
2nd row1.769.208,76
3rd row1.945.770,26
4th row1.623.637,76
5th row2.262.726,64
ValueCountFrequency (%)
2.904.070,46 1
 
0.4%
1.844.364,84 1
 
0.4%
1.623.637,76 1
 
0.4%
2.262.726,64 1
 
0.4%
1.824.276,55 1
 
0.4%
1.394.477,93 1
 
0.4%
1.972.910,20 1
 
0.4%
1.699.713,25 1
 
0.4%
1.583.854,01 1
 
0.4%
1.940.745,86 1
 
0.4%
Other values (271) 271
96.4%
2024-07-29T20:21:12.746144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 562
16.7%
2 368
10.9%
1 293
8.7%
, 281
8.3%
3 281
8.3%
4 255
7.6%
9 245
7.3%
8 226
6.7%
0 225
6.7%
5 222
 
6.6%
Other values (2) 414
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 562
16.7%
2 368
10.9%
1 293
8.7%
, 281
8.3%
3 281
8.3%
4 255
7.6%
9 245
7.3%
8 226
6.7%
0 225
6.7%
5 222
 
6.6%
Other values (2) 414
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 562
16.7%
2 368
10.9%
1 293
8.7%
, 281
8.3%
3 281
8.3%
4 255
7.6%
9 245
7.3%
8 226
6.7%
0 225
6.7%
5 222
 
6.6%
Other values (2) 414
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 562
16.7%
2 368
10.9%
1 293
8.7%
, 281
8.3%
3 281
8.3%
4 255
7.6%
9 245
7.3%
8 226
6.7%
0 225
6.7%
5 222
 
6.6%
Other values (2) 414
12.3%

Correlations

2024-07-29T20:21:12.794016image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ButanoDiesel ultra bajo azufreMTBENaftasOrimulsiónPetróleo crudoPetróleo reconstituido
Butano1.0000.5500.0000.0730.0000.0000.000
Diesel ultra bajo azufre0.5501.0000.0000.7750.0000.7990.000
MTBE0.0000.0001.0000.0000.0000.0000.983
Naftas0.0730.7750.0001.0000.0000.8340.000
Orimulsión0.0000.0000.0000.0001.0000.0000.000
Petróleo crudo0.0000.7990.0000.8340.0001.0000.000
Petróleo reconstituido0.0000.0000.9830.0000.0000.0001.000

Missing values

2024-07-29T20:21:07.041886image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-29T20:21:07.213597image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-29T20:21:07.336626image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FechaAceites lubricantesAsfaltoBunkerCerasCombustible turbo jetDiesel bajo azufreDiesel ultra bajo azufreGas licuado de petróleoGasolina de aviaciónGasolina regularGasolina superiorGrasas lubricantesKerosinaMezclas oleosasNaftasPetcokePetróleo crudoSolventesButanoDiesel alto azufreMTBEOrimulsiónPetróleo reconstituidoTotal importación
0ene/20010,0027.748,99214.581,840,000,000,000,00194.065,74820,00177.776,50373.963,960,0033.834,030,000,000,000,000,000,00566.101,998.402,000,00715.344,002.312.639,05
1feb/20010,007.503,57294.609,000,000,000,000,00170.703,383.054,00123.115,99243.091,070,0067.439,950,000,000,000,000,000,00489.525,800,000,00370.166,001.769.208,76
2mar/20010,0026.304,32315.263,800,000,000,000,00161.837,37677,00161.726,42312.084,380,0031.787,290,000,000,000,000,000,00575.559,680,000,00360.530,001.945.770,26
3abr/20010,007.885,89205.653,000,000,000,000,00163.048,643.399,00127.338,74285.054,890,0025.801,180,000,000,000,000,000,00437.745,428.184,000,00359.527,001.623.637,76
4may/20010,008.443,16278.371,300,000,000,000,00171.518,86585,00168.730,19300.913,670,0045.529,330,000,000,000,000,000,00552.609,1312.680,000,00723.346,002.262.726,64
5jun/20010,006.229,04218.765,600,000,000,000,00190.004,42492,00152.899,09333.217,190,0064.444,950,000,000,000,000,000,00497.855,260,000,00360.369,001.824.276,55
6jul/20010,003.103,99156.109,000,000,000,000,00206.022,83655,00136.299,13195.071,860,0023.654,100,000,000,000,000,000,00302.350,0210.642,000,00360.570,001.394.477,93
7ago/20010,0027.821,06189.692,600,000,000,000,00100.561,47607,00139.365,07268.153,260,0049.890,610,000,000,000,000,000,00464.159,1313.357,000,00719.303,001.972.910,20
8sept/20010,002.324,85385.142,300,000,000,000,00186.839,023.868,00233.643,75308.439,070,0041.519,910,000,000,000,000,000,00321.952,940,000,00360.635,001.844.364,84
9oct/20010,001.484,40214.138,000,000,000,000,00163.864,20650,00141.550,22305.102,280,0032.657,330,000,000,000,000,000,00438.989,5519.431,000,00717.717,002.035.583,98
FechaAceites lubricantesAsfaltoBunkerCerasCombustible turbo jetDiesel bajo azufreDiesel ultra bajo azufreGas licuado de petróleoGasolina de aviaciónGasolina regularGasolina superiorGrasas lubricantesKerosinaMezclas oleosasNaftasPetcokePetróleo crudoSolventesButanoDiesel alto azufreMTBEOrimulsiónPetróleo reconstituidoTotal importación
274nov/202327.025,8511.353,21146.036,206,8672.479,401.348.739,1619.959,80578.797,450,00839.290,02682.060,84656,660,002.742,5380,35505.238,393.715,0011.089,620,000,000,000,000,004.249.271,34
275dic/202326.971,619.898,92325.555,6213,31104.724,641.509.634,2822.953,87692.182,032.777,45763.754,27571.924,92202,910,001.600,660,12596.212,633.190,0021.033,990,000,000,000,000,004.652.631,23
276ene/202426.693,787.837,2999.680,22105,62101.415,501.409.097,156.710,98701.570,800,00914.133,32712.333,33332,390,002.879,59123,60264.918,620,0015.383,870,000,000,000,000,004.263.216,06
277feb/202421.374,615.313,11143.951,9595,0987.352,101.236.861,754.639,33916.541,705.978,37740.662,25650.360,11280,110,003.041,2080,00534.021,572.057,0024.524,030,000,000,000,000,004.377.134,28
278mar/202427.993,045.633,41163.119,060,00105.710,451.477.038,005.007,48675.157,480,00838.270,93620.077,74320,670,002.205,73263,28523.701,862.869,003.782,280,000,000,000,000,004.451.150,41
279abr/202437.444,607.990,9977.253,6533,5285.752,171.294.706,120,00473.940,663.351,23886.132,77687.017,961.094,830,002.980,9840,000,007.802,0023.555,380,000,000,000,000,003.589.096,86
280may/202431.035,5510.483,78544.682,1591,8394.606,001.470.870,090,00684.864,460,00939.656,18696.970,30212,240,002.678,22242,22518.800,003.845,0014.929,210,000,000,000,000,005.013.967,23
281NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
282Fuente: informes mensuales de titulares de licencias de la cadena de comercialización de hidrocarburos.NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
283Nota: Información sujeta a actualización por presentación extemporánea y rectificacion de informes mensuales, por parte de los sujetos obligados.NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN